How can I measure the performance of my RAG system?

The skill provides evaluation frameworks to measure key metrics such as answer accuracy, retrieval quality, and the groundedness of the generated responses.

How does this skill handle document chunking?

It provides multiple strategies including Recursive Character splitting, Token-based splitting, and Semantic Chunking to ensure context is preserved during retrieval.

What is RAG and why should I use it?

RAG (Retrieval-Augmented Generation) allows LLMs to access specific external knowledge, reducing hallucinations and providing contextually accurate, grounded responses based on your own documents.

Does it support hybrid search capabilities?

Yes, it includes advanced patterns for combining dense semantic retrieval with sparse keyword search (BM25) to improve the relevance of retrieved documents.

Which vector databases are supported by this skill?

This skill includes implementation patterns and configuration guides for Pinecone, Weaviate, Chroma, Qdrant, Milvus, and FAISS.

RAG Implementation Guide

Name: RAG Implementation Guide
Author: HermeticOrmus

byHermeticOrmus

데이터 과학 및 ML

Builds sophisticated Retrieval-Augmented Generation systems to ground LLM applications with proprietary data and external knowledge.

소개

This skill provides comprehensive patterns and best practices for implementing Retrieval-Augmented Generation (RAG) in AI applications. It guides developers through the entire pipeline, from document chunking and embedding generation to vector database configuration and advanced retrieval strategies like hybrid search and reranking. By following these implementation patterns, developers can build Q&A systems and documentation assistants that provide accurate, source-cited responses while significantly reducing model hallucinations and ensuring groundedness.

주요 기능

0 GitHub stars
Comprehensive vector database configurations for Pinecone, Chroma, and Weaviate
Advanced retrieval patterns such as Hybrid Search, Multi-Query, and Reranking
Optimized document chunking strategies including semantic and recursive splitting
Integration workflows for popular embedding models and reranking libraries
Built-in evaluation metrics for groundedness, accuracy, and retrieval quality

사용 사례

Developing research tools that require natural language search and source citations
Creating domain-specific chatbots grounded in frequently updated knowledge bases
Building a Q&A system for proprietary technical documentation or internal wikis

소개

주요 기능

0 GitHub stars
Comprehensive vector database configurations for Pinecone, Chroma, and Weaviate
Advanced retrieval patterns such as Hybrid Search, Multi-Query, and Reranking
Optimized document chunking strategies including semantic and recursive splitting
Integration workflows for popular embedding models and reranking libraries
Built-in evaluation metrics for groundedness, accuracy, and retrieval quality

사용 사례

Developing research tools that require natural language search and source citations
Creating domain-specific chatbots grounded in frequently updated knowledge bases
Building a Q&A system for proprietary technical documentation or internal wikis